The task of evaluating GANs is an area of active research, and a result of the fact that the optimization (driven by Eq. REF ) contains no explicit measure of sample quality other than the discriminator loss. Heusel et al. introduced a measure known as the Fréchet Inception Distance, whereby samples are embedded in a specific layer of an Inception-v3 network [1]} pretrained on ImageNet in order to produce embedded features. Two multivariate Gaussians are then fit to these features and the Fréchet Distance [2]}, or Wassterstein-2 distance [3]} between these two Gaussians ( fit to the real and fake embedded features) is calculated [4]} as:
\(FID(x, g) = ||\mu _{x} - \mu _{g}||^{2}_{2} + Tr(\Sigma _{x} + \Sigma _{g} - 2(\Sigma _{x}\Sigma _{g}))^{\frac{1}{2}}\) 
